The combination of multiple-input multiple-output (MIMO) technology and sparse code multiple access (SCMA) can significantly enhance the spectral efficiency of future wireless communication networks. However, the receiver design for downlink MIMO-SCMA systems faces challenges in developing multi-user detection (MUD) schemes that achieve both low latency and low bit error rate (BER). The separated detection scheme in the MIMO-SCMA system involves performing MIMO detection first to obtain estimated signals, followed by SCMA decoding. We propose an enhanced separated detection scheme based on lightweight graph neural networks (GNNs). In this scheme, we raise the concept of coordinate point relay and full-category training, which allow for the substitution of the conventional message passing algorithm (MPA) in SCMA decoding with image classification techniques based on deep learning (DL). The features of the images used for training encompass crucial information such as the amplitude and phase of estimated signals, as well as channel characteristics they have encountered. Furthermore, various types of images demonstrate distinct directional trends, contributing additional features that enhance the precision of classification by GNNs. Simulation results demonstrate that the enhanced separated detection scheme outperforms existing separated and joint detection schemes in terms of computational complexity, while having a better BER performance than the joint detection schemes at high Eb/N0 (energy per bit to noise power spectral density ratio) values.

It is well-known that, in DS-CDMA downlink signal transmission, frequency-domain equalization (FDE) based on minimum mean square error (MMSE) criterion can replace rake combining to achieve much improved bit error rate (BER) performance in severe frequency-selective fading channel. However, in uplink signal transmission, as each user's signal goes through a different channel, a severe multi-user interference (MUI) is produced and the uplink BER performance severely degrades compared to the downlink. When a small spreading factor is used, the uplink BER performance further degrades due to inter-chip interference (ICI). In this paper, we propose a frequency-domain multi-stage soft interference cancellation scheme for the DS-CDMA uplink and the achievable BER performance is evaluated by computer simulation. The BER performance comparison of the proposed cancellation technique and the multi-user detection (MUD) is also presented.

In this letter, we propose a multi-user detection scheme based on a hidden training sequence for DS-UWB systems. The hidden training sequence, which uses a fraction of the informative sequence's transmitting power as training information, is utilized for the receiver filter adaptation and channel estimation. By using this, the proposed scheme offers increased bandwidth efficiency (no period dedicated for training) and also shows reasonably good performance and near-far resistance in single and multiple-access UWB indoor multipath channel environment.